Algorithms and applications for approximate nonnegative matrix factorization
نویسندگان
چکیده
منابع مشابه
Algorithms and applications for approximate nonnegative matrix factorization
In this paper we discuss the development and use of low-rank approximate nonnegative matrix factorization (NMF) algorithms for feature extraction and identification in the fields of text mining and spectral data analysis. The evolution and convergence properties of hybrid methods based on both sparsity and smoothness constraints for the resulting nonnegative matrix factors are discussed. The in...
متن کاملApproximate Nonnegative Matrix Factorization via Alternating Minimization
In this paper we consider the Nonnegative Matrix Factorization (NMF) problem: given an (elementwise) nonnegative matrix V ∈ R + find, for assigned k, nonnegative matrices W ∈ R + and H ∈ R k×n + such that V = WH . Exact, non trivial, nonnegative factorizations do not always exist, hence it is interesting to pose the approximate NMF problem. The criterion which is commonly employed is I-divergen...
متن کاملNonnegative Matrix Factorization: Algorithms and Parallelization
An alternative to singular value decomposition (SVD) in the information retrieval is the low-rank approximation of an original non-negative matrix A by its non-negative factors U and V . The columns of U are the feature vectors with no non-negative components, and the columns of V store the non-negative weights that serve for the combination of feature vectors. First experiments show that restr...
متن کاملAlgorithms for Approximate Subtropical Matrix Factorization
Matrix factorization methods are important tools in data mining and analysis. They can be used for many tasks, ranging from dimensionality reduction to visualization. In this paper we concentrate on the use of matrix factorizations for finding patterns from the data. Rather than using the standard algebra – and the summation of the rank-1 components to build the approximation of the original ma...
متن کاملAlgorithms, Initializations, and Convergence for the Nonnegative Matrix Factorization
It is well-known that good initializations can improve the speed and accuracy of the solutions of many nonnegative matrix factorization (NMF) algorithms [56]. Many NMF algorithms are sensitive with respect to the initialization of W or H or both. This is especially true of algorithms of the alternating least squares (ALS) type [55], including the two new ALS algorithms that we present in this p...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2007
ISSN: 0167-9473
DOI: 10.1016/j.csda.2006.11.006